Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- Ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Propensity Scores Common Support

## Loading required package: lattice
## 
## Attaching package: 'lattice'
## The following object is masked from 'package:boot':
## 
##     melanoma
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## Attaching package: 'caret'
## The following object is masked from 'package:survival':
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Model Stats

  • Treatment proportion: 0.127
  • Model Type: elastic_net
  • Accuracy: 0.9026204
  • Params: alpha: 0.6769231 lambda: 0.0016797

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-72.00 -21.00 -10.63  -4.00  30.80 
Model Type Y: boosting 
RMSE: 20.3649460544989 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.9444444

Model Type No: boosting 
RMSE: 13.259844472241 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6666667

ATE (Yes-No): 0.133 (Std.Error: 3.234)
Trimmed ATE (Yes-No): 0.275 (Std.Error: 3.42)
Upper ATE (Yes-No): -3.254 (Std.Error: 6.646)
Observational differences in treatment 3.397 (Yes-No) 

   treatment  outcome
1:       Yes 25.23682
2:        No 21.83957
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-64.00 -22.47 -10.10  -3.00  22.44 
Model Type Y: boosting 
RMSE: 20.6251283691844 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.031848787254 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -2.621 (Std.Error: 6.972)
Trimmed ATE (Yes-No): -2.437 (Std.Error: 7.255)
Upper ATE (Yes-No): -6.599 (Std.Error: 5.895)
Observational differences in treatment 2.873 (Yes-No) 

   treatment  outcome
1:       Yes 23.74424
2:        No 20.87154
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -6.000000  -1.496444   1.722212  18.000000 
Model Type Y: boosting 
RMSE: 6.51940913373727 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.09759680657004 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5555556

ATE (Yes-No): -5.002 (Std.Error: 1.705)
Trimmed ATE (Yes-No): -5.157 (Std.Error: 1.739)
Upper ATE (Yes-No): -1.183 (Std.Error: 3.912)
Observational differences in treatment -1.028 (Yes-No) 

   treatment   outcome
1:       Yes -3.468840
2:        No -2.441266
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -6.000000  -2.009266   1.134408  20.000000 
Model Type Y: boosting 
RMSE: 7.6860428127509 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 5.95380054504046 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -3.579 (Std.Error: 1.769)
Trimmed ATE (Yes-No): -3.538 (Std.Error: 1.866)
Upper ATE (Yes-No): -4.294 (Std.Error: 3.354)
Observational differences in treatment -0.702 (Yes-No) 

   treatment   outcome
1:       Yes -3.307045
2:        No -2.605153
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-194.7900  -69.0225  -27.8500    1.9650  114.1500 
Model Type Y: boosting 
RMSE: 64.258387891307 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6111111

Model Type No: boosting 
RMSE: 53.8439139921516 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -44.694 (Std.Error: 11.167)
Trimmed ATE (Yes-No): -46.002 (Std.Error: 11.669)
Upper ATE (Yes-No): -18.255 (Std.Error: 32.894)
Observational differences in treatment -8.555 (Yes-No) 

   treatment  outcome
1:       Yes 26.17093
2:        No 34.72625
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Sagittal Balance
Distribution:
      0%      25%      50%      75%     100% 
-237.470  -67.310  -30.510    5.985  109.540 
Model Type Y: boosting 
RMSE: 57.0465301621959 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6111111

Model Type No: boosting 
RMSE: 53.8877689391649 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -37.385 (Std.Error: 12.604)
Trimmed ATE (Yes-No): -37.008 (Std.Error: 13.148)
Upper ATE (Yes-No): -44.376 (Std.Error: 36.88)
Observational differences in treatment -12.482 (Yes-No) 

   treatment  outcome
1:       Yes 25.00806
2:        No 37.48991
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-68.620 -18.035  -6.000   1.610 149.410 
Model Type Y: boosting 
RMSE: 14.7315439404391 
Params: nrounds: 50.0
max_depth: 2
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 14.4506400312463 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -9.393 (Std.Error: 3.239)
Trimmed ATE (Yes-No): -9.538 (Std.Error: 3.293)
Upper ATE (Yes-No): -6.064 (Std.Error: 6.654)
Observational differences in treatment -5.722 (Yes-No) 

   treatment  outcome
1:       Yes 19.65955
2:        No 25.38121
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Global Tilt
Distribution:
    0%    25%    50%    75%   100% 
-62.63 -16.94  -6.21   1.00  26.00 
Model Type Y: boosting 
RMSE: 14.9372868381204 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5555556

Model Type No: boosting 
RMSE: 12.0788379845415 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -13.424 (Std.Error: 3.686)
Trimmed ATE (Yes-No): -13.569 (Std.Error: 3.876)
Upper ATE (Yes-No): -10.564 (Std.Error: 7.64)
Observational differences in treatment -5.435 (Yes-No) 

   treatment  outcome
1:       Yes 20.46031
2:        No 25.89494
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.930 -24.000  -9.635   0.140  29.000 
Model Type Y: boosting 
RMSE: 21.8485525180169 
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.6908306938334 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -5.543 (Std.Error: 4.587)
Trimmed ATE (Yes-No): -5.675 (Std.Error: 4.862)
Upper ATE (Yes-No): -2.376 (Std.Error: 9.971)
Observational differences in treatment -1.998 (Yes-No) 

   treatment   outcome
1:       Yes -51.05795
2:        No -49.05961
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.630 -25.000  -8.230  -0.015  23.380 
Model Type Y: boosting 
RMSE: 22.8284732418272 
Params: nrounds: 50.0
max_depth: 2
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7777778

Model Type No: boosting 
RMSE: 15.2950703590457 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5555556

ATE (Yes-No): -14.138 (Std.Error: 6.38)
Trimmed ATE (Yes-No): -14.391 (Std.Error: 6.716)
Upper ATE (Yes-No): -8.659 (Std.Error: 9.017)
Observational differences in treatment 0.145 (Yes-No) 

   treatment   outcome
1:       Yes -49.32812
2:        No -49.47321
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-96.1234 -24.0000  -9.1601   0.4592  78.9200 
Model Type Y: boosting 
RMSE: 20.7795506532589 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.8888889

Model Type No: boosting 
RMSE: 17.502483307831 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -6.326 (Std.Error: 3.979)
Trimmed ATE (Yes-No): -6.521 (Std.Error: 4.116)
Upper ATE (Yes-No): -1.657 (Std.Error: 8.123)
Observational differences in treatment -3.364 (Yes-No) 

   treatment  outcome
1:       Yes 11.29544
2:        No 14.65980
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. LGap
Distribution:
      0%      25%      50%      75%     100% 
-94.8082 -25.0000  -8.5790  -0.1606  22.0800 
Model Type Y: boosting 
RMSE: 22.2333080123564 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.7576189801855 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5555556

ATE (Yes-No): -13.227 (Std.Error: 5.325)
Trimmed ATE (Yes-No): -13.599 (Std.Error: 5.48)
Upper ATE (Yes-No): -5.283 (Std.Error: 10.799)
Observational differences in treatment -2.197 (Yes-No) 

   treatment  outcome
1:       Yes 11.43743
2:        No 13.63423
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
    0%    25%    50%    75%   100% 
-36.41  -8.59  -2.53   2.11  14.42 
Model Type Y: boosting 
RMSE: 9.90457064459919 
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6111111

Model Type No: boosting 
RMSE: 7.71820971299284 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6666667

ATE (Yes-No): -3.773 (Std.Error: 2.213)
Trimmed ATE (Yes-No): -3.795 (Std.Error: 2.272)
Upper ATE (Yes-No): -3.189 (Std.Error: 4.871)
Observational differences in treatment -3.393 (Yes-No) 

   treatment  outcome
1:       Yes 18.57233
2:        No 21.96521
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-26.620  -7.160  -2.205   1.945  23.000 
Model Type Y: boosting 
RMSE: 9.8511269107088 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.8440355941008 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5555556

ATE (Yes-No): -7.774 (Std.Error: 2.365)
Trimmed ATE (Yes-No): -8.067 (Std.Error: 2.438)
Upper ATE (Yes-No): -1.531 (Std.Error: 3.842)
Observational differences in treatment -3.96 (Yes-No) 

   treatment  outcome
1:       Yes 18.76625
2:        No 22.72589
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'